Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.3 KiB
Average record size in memory371.2 B

Variable types

DateTime1
Numeric7
Categorical3

Alerts

year has constant value ""Constant
month has constant value ""Constant
day has constant value ""Constant
Close is highly overall correlated with High and 2 other fieldsHigh correlation
High is highly overall correlated with Close and 2 other fieldsHigh correlation
Low is highly overall correlated with Close and 2 other fieldsHigh correlation
Open is highly overall correlated with Close and 2 other fieldsHigh correlation
timestamp has unique valuesUnique
Volume has unique valuesUnique
minute has 8 (8.0%) zerosZeros

Reproduction

Analysis started2024-02-03 18:28:27.669813
Analysis finished2024-02-03 18:29:06.551006
Duration38.88 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

timestamp
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Minimum2024-02-02 11:40:00
Maximum2024-02-02 19:55:00
2024-02-03T23:59:06.635421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:06.755462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.2324
Minimum409.23
Maximum412.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:06.881033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum409.23
5-th percentile409.90125
Q1411.0275
median411.285
Q3411.54
95-th percentile412.01
Maximum412.54
Range3.31
Interquartile range (IQR)0.5125

Descriptive statistics

Standard deviation0.55763461
Coefficient of variation (CV)0.0013560085
Kurtosis2.3873293
Mean411.2324
Median Absolute Deviation (MAD)0.255
Skewness-1.1315671
Sum41123.24
Variance0.31095636
MonotonicityNot monotonic
2024-02-03T23:59:06.998364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411.435 4
 
4.0%
411.1 4
 
4.0%
412.01 3
 
3.0%
410.82 3
 
3.0%
411 3
 
3.0%
411.54 3
 
3.0%
411.45 2
 
2.0%
411.325 2
 
2.0%
411.19 2
 
2.0%
411.465 2
 
2.0%
Other values (66) 72
72.0%
ValueCountFrequency (%)
409.23 1
1.0%
409.61 1
1.0%
409.79 1
1.0%
409.828 1
1.0%
409.83 1
1.0%
409.905 1
1.0%
409.97 1
1.0%
410.28 1
1.0%
410.54 1
1.0%
410.645 1
1.0%
ValueCountFrequency (%)
412.54 1
 
1.0%
412.23 1
 
1.0%
412.08 1
 
1.0%
412.01 3
3.0%
412 1
 
1.0%
411.97 1
 
1.0%
411.92 1
 
1.0%
411.91 1
 
1.0%
411.81 1
 
1.0%
411.76 1
 
1.0%

High
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.51252
Minimum409.631
Maximum415.318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:07.122124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum409.631
5-th percentile410.309
Q1411.22
median411.49
Q3411.74625
95-th percentile412.2735
Maximum415.318
Range5.687
Interquartile range (IQR)0.52625

Descriptive statistics

Standard deviation0.65363701
Coefficient of variation (CV)0.001588377
Kurtosis11.579244
Mean411.51252
Median Absolute Deviation (MAD)0.265
Skewness1.4715199
Sum41151.252
Variance0.42724134
MonotonicityNot monotonic
2024-02-03T23:59:07.238023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411.22 6
 
6.0%
411.45 6
 
6.0%
411.5 5
 
5.0%
411.65 4
 
4.0%
411.3 3
 
3.0%
411.49 3
 
3.0%
411.2 3
 
3.0%
411.39 2
 
2.0%
412.11 2
 
2.0%
411.4 2
 
2.0%
Other values (58) 64
64.0%
ValueCountFrequency (%)
409.631 1
1.0%
409.99 1
1.0%
410.06 1
1.0%
410.08 1
1.0%
410.29 1
1.0%
410.31 1
1.0%
410.38 1
1.0%
410.81 1
1.0%
410.89 1
1.0%
410.95 1
1.0%
ValueCountFrequency (%)
415.318 1
1.0%
412.65 1
1.0%
412.63 1
1.0%
412.42 1
1.0%
412.34 1
1.0%
412.27 1
1.0%
412.23 1
1.0%
412.22 1
1.0%
412.202 1
1.0%
412.15 1
1.0%

Low
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.00971
Minimum409.08
Maximum412.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:07.361145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum409.08
5-th percentile409.709
Q1410.8425
median411.04
Q3411.38
95-th percentile411.7005
Maximum412.005
Range2.925
Interquartile range (IQR)0.5375

Descriptive statistics

Standard deviation0.54949715
Coefficient of variation (CV)0.0013369444
Kurtosis1.8366955
Mean411.00971
Median Absolute Deviation (MAD)0.272
Skewness-1.229793
Sum41100.971
Variance0.30194712
MonotonicityNot monotonic
2024-02-03T23:59:07.478253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411.01 7
 
7.0%
411.02 5
 
5.0%
410.89 4
 
4.0%
411.4 3
 
3.0%
411 3
 
3.0%
411.38 3
 
3.0%
410.8 2
 
2.0%
411.12 2
 
2.0%
411.21 2
 
2.0%
411.18 2
 
2.0%
Other values (61) 67
67.0%
ValueCountFrequency (%)
409.08 1
1.0%
409.6 1
1.0%
409.64 2
2.0%
409.69 1
1.0%
409.71 1
1.0%
409.75 1
1.0%
409.83 1
1.0%
409.94 1
1.0%
410.18 1
1.0%
410.28 1
1.0%
ValueCountFrequency (%)
412.005 1
1.0%
411.92 1
1.0%
411.88 1
1.0%
411.72 1
1.0%
411.71 1
1.0%
411.7 1
1.0%
411.69 1
1.0%
411.63 1
1.0%
411.605 1
1.0%
411.59 2
2.0%

Close
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.25441
Minimum409.62
Maximum412.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:07.600987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum409.62
5-th percentile409.9375
Q1411.09
median411.29
Q3411.53625
95-th percentile412.0105
Maximum412.51
Range2.89
Interquartile range (IQR)0.44625

Descriptive statistics

Standard deviation0.51859011
Coefficient of variation (CV)0.0012609959
Kurtosis2.1013074
Mean411.25441
Median Absolute Deviation (MAD)0.235
Skewness-0.98121914
Sum41125.441
Variance0.2689357
MonotonicityNot monotonic
2024-02-03T23:59:07.715177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411.15 3
 
3.0%
411.01 3
 
3.0%
411.53 3
 
3.0%
411.29 2
 
2.0%
411.32 2
 
2.0%
411.17 2
 
2.0%
411.62 2
 
2.0%
411.155 2
 
2.0%
411.09 2
 
2.0%
411.22 2
 
2.0%
Other values (73) 77
77.0%
ValueCountFrequency (%)
409.62 1
1.0%
409.78 1
1.0%
409.805 1
1.0%
409.822 1
1.0%
409.89 1
1.0%
409.94 1
1.0%
410.275 1
1.0%
410.45 1
1.0%
410.71 1
1.0%
410.752 1
1.0%
ValueCountFrequency (%)
412.51 1
1.0%
412.25 1
1.0%
412.055 1
1.0%
412.03 1
1.0%
412.02 1
1.0%
412.01 1
1.0%
411.99 1
1.0%
411.97 1
1.0%
411.941 1
1.0%
411.935 1
1.0%

Volume
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209817.59
Minimum130
Maximum8214776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:07.840616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile285.95
Q11705.5
median125568.5
Q3196226.5
95-th percentile392885.3
Maximum8214776
Range8214646
Interquartile range (IQR)194521

Descriptive statistics

Standard deviation824435.74
Coefficient of variation (CV)3.9292975
Kurtosis92.31299
Mean209817.59
Median Absolute Deviation (MAD)122568
Skewness9.4425777
Sum20981759
Variance6.7969429 × 1011
MonotonicityNot monotonic
2024-02-03T23:59:07.956488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1334 1
 
1.0%
150803 1
 
1.0%
127623 1
 
1.0%
105652 1
 
1.0%
153111 1
 
1.0%
118496 1
 
1.0%
147100 1
 
1.0%
162362 1
 
1.0%
312422 1
 
1.0%
111519 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
130 1
1.0%
168 1
1.0%
198 1
1.0%
243 1
1.0%
247 1
1.0%
288 1
1.0%
329 1
1.0%
339 1
1.0%
462 1
1.0%
556 1
1.0%
ValueCountFrequency (%)
8214776 1
1.0%
1099519 1
1.0%
613253 1
1.0%
439529 1
1.0%
437484 1
1.0%
390538 1
1.0%
348962 1
1.0%
339107 1
1.0%
336975 1
1.0%
328177 1
1.0%

year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2024
100 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 100
100.0%

Length

2024-02-03T23:59:08.058302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-03T23:59:08.148517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2024 100
100.0%

Most occurring characters

ValueCountFrequency (%)
2 200
50.0%
0 100
25.0%
4 100
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 200
50.0%
0 100
25.0%
4 100
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 200
50.0%
0 100
25.0%
4 100
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 200
50.0%
0 100
25.0%
4 100
25.0%

month
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
100.0%

Length

2024-02-03T23:59:08.218334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-03T23:59:08.299939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 100
100.0%

Most occurring characters

ValueCountFrequency (%)
2 100
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 100
100.0%

day
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
100.0%

Length

2024-02-03T23:59:08.369408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-03T23:59:08.451553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 100
100.0%

Most occurring characters

ValueCountFrequency (%)
2 100
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 100
100.0%

hour
Real number (ℝ)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.32
Minimum11
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:08.515564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q113
median15
Q317
95-th percentile19
Maximum19
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4241212
Coefficient of variation (CV)0.15823245
Kurtosis-1.1807225
Mean15.32
Median Absolute Deviation (MAD)2
Skewness-0.036009075
Sum1532
Variance5.8763636
MonotonicityDecreasing
2024-02-03T23:59:08.596650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
19 12
12.0%
18 12
12.0%
17 12
12.0%
16 12
12.0%
15 12
12.0%
14 12
12.0%
13 12
12.0%
12 12
12.0%
11 4
 
4.0%
ValueCountFrequency (%)
11 4
 
4.0%
12 12
12.0%
13 12
12.0%
14 12
12.0%
15 12
12.0%
16 12
12.0%
17 12
12.0%
18 12
12.0%
19 12
12.0%
ValueCountFrequency (%)
19 12
12.0%
18 12
12.0%
17 12
12.0%
16 12
12.0%
15 12
12.0%
14 12
12.0%
13 12
12.0%
12 12
12.0%
11 4
 
4.0%

minute
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.3
Minimum0
Maximum55
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-02-03T23:59:08.684228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median30
Q345
95-th percentile55
Maximum55
Range55
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.483325
Coefficient of variation (CV)0.61778535
Kurtosis-1.2402981
Mean28.3
Median Absolute Deviation (MAD)15
Skewness-0.063989921
Sum2830
Variance305.66667
MonotonicityNot monotonic
2024-02-03T23:59:08.758632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
55 9
9.0%
50 9
9.0%
45 9
9.0%
40 9
9.0%
35 8
8.0%
30 8
8.0%
25 8
8.0%
20 8
8.0%
15 8
8.0%
10 8
8.0%
Other values (2) 16
16.0%
ValueCountFrequency (%)
0 8
8.0%
5 8
8.0%
10 8
8.0%
15 8
8.0%
20 8
8.0%
25 8
8.0%
30 8
8.0%
35 8
8.0%
40 9
9.0%
45 9
9.0%
ValueCountFrequency (%)
55 9
9.0%
50 9
9.0%
45 9
9.0%
40 9
9.0%
35 8
8.0%
30 8
8.0%
25 8
8.0%
20 8
8.0%
15 8
8.0%
10 8
8.0%

Interactions

2024-02-03T23:59:02.302131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:27.889921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:34.737789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:40.500894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:46.300449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:52.512296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:58.480717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:03.228468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:28.897473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:35.761378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:41.453984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:47.427693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:53.523112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:59.207051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:03.878827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:29.816539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:36.882539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:42.554307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:48.379459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:54.459840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:59.858446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:04.550927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:30.904305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:37.798060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:43.472737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:49.361917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:55.425449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:00.542066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:05.334984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:32.085163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:38.833924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:44.483808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:50.423483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:56.517211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:01.314920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:06.123453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:33.207933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:39.847094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:45.524795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:51.506789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:57.699323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:02.114287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:06.213814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:34.350701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:40.179435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:45.965456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:52.131571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:58:58.095208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-02-03T23:59:02.213215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-02-03T23:59:08.841045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
CloseHighLowOpenVolumehourminute
Close1.0000.8200.8760.765-0.296-0.038-0.058
High0.8201.0000.7820.832-0.276-0.076-0.105
Low0.8760.7821.0000.870-0.3140.104-0.076
Open0.7650.8320.8701.000-0.337-0.011-0.052
Volume-0.296-0.276-0.314-0.3371.0000.155-0.123
hour-0.038-0.0760.104-0.0110.1551.000-0.078
minute-0.058-0.105-0.076-0.052-0.123-0.0781.000

Missing values

2024-02-03T23:59:06.338895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-03T23:59:06.482750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestampOpenHighLowCloseVolumeyearmonthdayhourminute
02024-02-02 19:55:00411.0500411.2200410.9800411.140013342024221955
12024-02-02 19:50:00411.0000411.2200410.9400411.090016592024221950
22024-02-02 19:45:00411.1850411.2200410.8900411.080014302024221945
32024-02-02 19:40:00411.2100411.4000411.0000411.210025442024221940
42024-02-02 19:35:00411.0200411.3000411.0100411.20507032024221935
52024-02-02 19:30:00411.1700411.4500410.8900411.22502882024221930
62024-02-02 19:25:00411.1000411.4500410.8900411.17001682024221925
72024-02-02 19:20:00410.9500411.5000410.8900411.10503292024221920
82024-02-02 19:15:00411.0800411.1500410.8800411.01003392024221915
92024-02-02 19:10:00411.2800411.3000411.0100411.15004622024221910
timestampOpenHighLowCloseVolumeyearmonthdayhourminute
902024-02-02 12:25:00410.7500411.2320410.6400411.17001748392024221225
912024-02-02 12:20:00411.0600411.1100410.6300410.75201940262024221220
922024-02-02 12:15:00409.7900411.1050409.6900411.06003905382024221215
932024-02-02 12:10:00409.9050410.0600409.6400409.78001469722024221210
942024-02-02 12:05:00409.9700410.0800409.7500409.8900201289202422125
952024-02-02 12:00:00409.8300410.3800409.8300409.9400189132202422120
962024-02-02 11:55:00410.2800410.2900409.6400409.82202292292024221155
972024-02-02 11:50:00409.8280410.3100409.7100410.27503489622024221150
982024-02-02 11:45:00409.6100409.9900409.6000409.80502965842024221145
992024-02-02 11:40:00409.2300409.6310409.0800409.62003281772024221140